Window Mask for Possible Object Location Generation

Possible object location generation is an important pre-process for most object detection algorithms. In this thesis, we design a window sampling algorithm to address possible object location generation problem in two steps. First, we use a two-phase feature space partition method to achieve local descriptor classification and find interest points on image which have high probability to be on object of interest. Then we introduced a way to learn the relationship between object bounding window and bag-of-words representation of local image region, with which we can sample windows that are highly possible to contain an object. We implement the algorithm in MATLAB and test it on Graz-02 dataset, which has three object categories: car, bike, human. The algorithm achieves state-of-the-art performance according to coverage, window quality, number of windows and running time. The MATLAB scripts are merged into one file called ``WindowMaskCode.pdf'', which can be found in supplementary files.